Third Workshop on Syntax and Structure in Statistical Translation (SSST-3)

The Third Workshop on Syntax and Structure in Statistical Translation (SSST-3) seeks to build on the foundations established in the first two SSST workshops, which brought together a large number of researchers working on diverse aspects of synchronous/transduction grammars (hereafter, S/TGs) in relation to statistical machine translation. Its program each year has comprised high-quality papers discussing current work spanning topics including: new grammatical models of translation; new learning methods for syntax-based models; using S/TGs for semantics and generation; syntax-based evaluation of machine translation; and formal properties of S/TGs. Presentations have led to productive and thought-provoking discussions, comparing and contrasting different approaches, and identifying the questions that are most pressing for future progress in this topic.

The need for structural mappings between languages is widely recognized in
the fields of statistical machine translation and spoken language
translation, and there is a growing consensus that these mappings are
appropriately represented using a family of formalisms that includes
synchronous/transduction grammars and their tree-transducer equivalents. To
date, flat-structured models, such as the word-based IBM models of the early
1990s or the more recent phrase-based models, remain widely used. But
tree-structured mappings arguably offer a much greater potential for learning
valid generalizations about relationships between languages.

Within this area of research there is a rich diversity of approaches.
There is active research ranging from formal properties of S/TGs to
large-scale end-to-end systems. There are approaches that make heavy use of
linguistic theory, and approaches that use little or none. There is
theoretical work characterizing the expressiveness and complexity of
particular formalisms, as well as empirical work assessing their modeling
accuracy and descriptive adequacy across various language pairs. There is
work being done to invent better translation models, and work to design
better algorithms. Recent years have seen significant progress on all these
fronts. In particular, systems based on these formalisms are now top
contenders in MT evaluations.